This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Dataset

This dataset was created by Rajdeep Kaur Bajwa

Contents

Dataset

This dataset was created by Jerraldo1705

Contents

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

This dataset is about books. It has 1 row and is filtered where the book is An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch. It features 7 columns including author, publication date, language, and book publisher.

Dataset

This dataset was created by Nargis Karimova

Released under CC0: Public Domain

Contents

R code for running GLMM and BRT analysis

ABOUT DATASET

This is the R markdown notebook. It contains step by step guide for working on Data Analysis with R. It helps you with installing the relevant packages and how to load them. it also provides a detailed summary of the "dplyr" commands that you can use to manipulate your data in the R environment.

Anyone new to R and wish to carry out some data analysis on R can check it out!

R scripts to re-analyze data published in Melo et al., 2020. We recommend using the R studio interface to run the code in these scripts. The script already contains the data table; therefore, there is no need to set the data tables in the script. Data table files are only available for better visualization between wide and long formats. Reference: Melo, M. B.; Favaro, V. M.; Oliveira, M. G. M (2020). The dorsal subiculum is required for the contextual fear consolidation in rats. Behavioural Brain Research. 390:112661. Doi: https://doi.org/10.1016/j.bbr.2020.112661.

The whole data and source can be found at https://emilhvitfeldt.github.io/friends/

"The goal of friends to provide the complete script transcription of the Friends sitcom. The data originates from the Character Mining repository which includes references to scientific explorations using this data. This package simply provides the data in tibble format instead of json files."

Content

• friends.csv - Contains the scenes and lines for each character, including season and episodes.
• friends_emotions.csv - Contains sentiments for each scene - for the first four seasons only.
• friends_info.csv - Contains information regarding each episode, such as imdb_rating, views, episode title and directors.

Uses

• Text mining, sentiment analysis and word statistics.
• Data visualizations.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Exploratory data analysis and visualisation of datasets

Time series analysis of climate data using R

The complexity of contexts and varied purposes for which biome donation are requested is unknown in South Africa. The aim of this study was to provide strategic data towards actualisation of whether a gastrointestinal (GIT) stool donor bank may be established as a collaborative between Western Cape Blood Services (WCBS) and the University of Cape Town (UCT).We designed a cross-sectional, questionnaire-based survey to determine willingness of WCBS blood donors to donate stool specimens for microbiome biobanking. The prospective observational pilot study was conducted between 1 June 2022 and 1 July 2022 at three WCBS donation centres in Cape Town, South Africa. Anonymous blood donors who met the inclusion criteria were provided with infographics on stool donation and a stool collection kit. Anonymised demographic and interview data was aggregated for descriptive purposes, and for statistical analysis.Analysis of responses from 209/231 blood donors demonstrated in a logistic regression model that compensation (p = 3.139e-05) and ' societal benefit outweighs inconvenience’ beliefs (p = 7.751e-05) were covariates significantly associated with willingness to donate stool. Age was borderline significant at a 5% level (p = 0.0556). Most willing stool donors indicated that donating stool samples would not affect blood donations (140/157, 90%). Factors decreasing willingness to donate were stool collection being unpleasant or embarrassing.The survey provides strategic data for the WCBS and UCT towards establishment of a stool bank and provided an understanding of the underlying determinants governing participants decision process with regards to becoming potential donors.

In a world of increasing crime, many organizations are interested in examining incident details to learn from and prevent future crime. Our client, based in Los Angeles County, was interested in this exact thing. They asked us to examine the data to answer several questions; among them, what was the rate of increase or decrease in crime from 2020 to 2023, and which ethnicity or group of people were targeted the most.

Our data was collected from Kaggle.com at the following link:

https://www.kaggle.com/datasets/nathaniellybrand/los-angeles-crime-dataset-2020-present

It was cleaned, examined for further errors, and the analysis performed using RStudio. The results of this analysis are in the attached PDF entitled: "crime_data_analysis_report." Please feel free to review the results as well as follow along with the dataset on your own machine.

Reproduces all analyses and plots using the dataset in S3 File. (RMD)

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can come from many different sources and be presented in various formats. Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles.